The globally-optimal iterative closest point (Go-ICP) [1]} is run on \(\mathcal {G}^{A}\)  and \(\mathcal {G}^{B}\)  as coarse geometrical initialisation. Here, the dense cloud of samples, i.e. the nodes coordinates \(\lbrace \mathbf {c}^{A}_{i},\mathbf {c}^{B}_{j}\rbrace \)  and the sequences of edge points \(\lbrace \mathbf {p}^{A}_{v},\mathbf {p}^{B}_{w}\rbrace \) , is retrieved for the spatial rigid pre-alignment. Go-ICP searches the entire 3D motion space, and, under the minimisation of an \(L_{2}\)  error metric based on a branch-and-bound scheme, guarantees the global optimality of the rigid mapping, even in presence of noisy data, outliers, and partial samples overlap.
